scholarly journals Variability and Reproducibility of Directed and Undirected Functional MRI Connectomes in the Human Brain

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 661 ◽  
Author(s):  
Allegra Conti ◽  
Andrea Duggento ◽  
Maria Guerrisi ◽  
Luca Passamonti ◽  
Iole Indovina ◽  
...  
Keyword(s):  
Human Brain ◽  
Functional Mri ◽  
Pearson Correlation ◽  
Related Information ◽  
Matrix Weights ◽  
Graph Metrics ◽  
Large N ◽  
Subject Variability ◽  
Key Factor ◽  
Global And Local

A growing number of studies are focusing on methods to estimate and analyze the functional connectome of the human brain. Graph theoretical measures are commonly employed to interpret and synthesize complex network-related information. While resting state functional MRI (rsfMRI) is often employed in this context, it is known to exhibit poor reproducibility, a key factor which is commonly neglected in typical cohort studies using connectomics-related measures as biomarkers. We aimed to fill this gap by analyzing and comparing the inter- and intra-subject variability of connectivity matrices, as well as graph-theoretical measures, in a large (n = 1003) database of young healthy subjects which underwent four consecutive rsfMRI sessions. We analyzed both directed (Granger Causality and Transfer Entropy) and undirected (Pearson Correlation and Partial Correlation) time-series association measures and related global and local graph-theoretical measures. While matrix weights exhibit a higher reproducibility in undirected, as opposed to directed, methods, this difference disappears when looking at global graph metrics and, in turn, exhibits strong regional dependence in local graphs metrics. Our results warrant caution in the interpretation of connectivity studies, and serve as a benchmark for future investigations by providing quantitative estimates for the inter- and intra-subject variabilities in both directed and undirected connectomic measures.

scholarly journals Variability and Reproducibility of Directed and Undirected Functional MRI Connectomes in the Human Brain

2019 ◽  
Author(s):  
Allegra Conti ◽  
Andrea Duggento ◽  
Maria Guerrisi ◽  
Luca Passamonti ◽  
Iole Indovina ◽  
...  
Keyword(s):  
Human Brain ◽  
Functional Mri ◽  
Pearson Correlation ◽  
Related Information ◽  
Matrix Weights ◽  
Graph Metrics ◽  
Large N ◽  
Subject Variability ◽  
Key Factor ◽  
Global And Local

A growing number of studies focus on methods to estimate and analyze the functional connectome of the human brain. Graph theoretical measures are commonly employed to interpret and synthesize complex network-related information. While resting state functional MRI (rsfMRI) is often employed in this context, is known to exhibit poor reproducibility, a key factor which is commonly neglected in typical cohort studies using connectomics-related measures as biomarkers. We aimed to fill this gap by analyzing and comparing inter- and intra- subject variability of connectivity matrices as well as graph-theoretical measures in a large (n=1003) database of young healthy subjects which underwent four consecutive rsfMRI sessions. We analyzed both directed (Granger Causality and Transfer Entropy) and undirected (Pearson Correlation and Partial Correlation) time-series association measures and related global and local graph-theoretical measures. While matrix weights exhibit a higher reproducibility in undirected as opposed to directed methods, this difference disappears when looking at global graph metrics and, in turn, exhibits strong regional dependence in local graphs metrics. Our results warrant caution in the interpretation of connectivity studies, and serve as a benchmark for future investigations by providing quantitative estimates for the inter- and intra- subject variabilities in both directed and undirected connectomic measures.

scholarly journals Signal quality as Achilles’ heel of graph theory in functional magnetic resonance imaging in multiple sclerosis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Johan Baijot ◽  
Stijn Denissen ◽  
Lars Costers ◽  
Jeroen Gielen ◽  
Melissa Cambron ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Signal To Noise Ratio ◽  
Pearson Correlation ◽  
Signal Quality ◽  
Network Parameters ◽  
Fmri Signal ◽  
Noise Ratio ◽  
Global And Local ◽  
The Brain ◽  
Local Brain

AbstractGraph-theoretical analysis is a novel tool to understand the organisation of the brain.We assessed whether altered graph theoretical parameters, as observed in multiple sclerosis (MS), reflect pathology-induced restructuring of the brain's functioning or result from a reduced signal quality in functional MRI (fMRI). In a cohort of 49 people with MS and a matched group of 25 healthy subjects (HS), we performed a cognitive evaluation and acquired fMRI. From the fMRI measurement, Pearson correlation-based networks were calculated and graph theoretical parameters reflecting global and local brain organisation were obtained. Additionally, we assessed metrics of scanning quality (signal to noise ratio (SNR)) and fMRI signal quality (temporal SNR and contrast to noise ratio (CNR)). In accordance with the literature, we found that the network parameters were altered in MS compared to HS. However, no significant link was found with cognition. Scanning quality (SNR) did not differ between both cohorts. In contrast, measures of fMRI signal quality were significantly different and explained the observed differences in GTA parameters. Our results suggest that differences in network parameters between MS and HS in fMRI do not reflect a functional reorganisation of the brain, but rather occur due to reduced fMRI signal quality.

Functional MRI of the human brain

Neuroreport ◽  
1994 ◽  
Vol 5 (7) ◽  
pp. 813-816 ◽  
Author(s):  
Christoph Segebarth ◽  
Valérie Belle ◽  
Chantai Delon ◽  
Raphaël Massarelli ◽  
Jean Decety ◽  
...  
Keyword(s):  
Human Brain ◽  
Functional Mri

scholarly journals Dynamic and stable brain connectivity during movie watching as revealed by functional MRI

2021 ◽  
Author(s):  
Xin Di ◽  
Zhiguo Zhang ◽  
Ting Xu ◽  
Bharat B. Biswal
Keyword(s):  
Functional Mri ◽  
Brain Connectivity ◽  
Brain Regions ◽  
Posterior Cingulate Cortex ◽  
Temporoparietal Junction ◽  
Related Information ◽  
Movie Clip ◽  
Brain Changes ◽  
Brain Processing ◽  
Dynamic Connectivity

AbstractSpatially remote brain regions show synchronized activity as typically revealed by correlated functional MRI (fMRI) signals. An emerging line of research has focused on the temporal fluctuations of connectivity, however, its relationships with stable connectivity have not been clearly illustrated. We examined the stable and dynamic connectivity from fMRI data when the participants watched four different movie clips. Using inter-individual correlation, we were able to estimate functionally meaningful dynamic connectivity associated with different movies. Widespread consistent dynamic connectivity was observed for each movie clip as well as their differences between clips. A cartoon movie clip showed higher consistent dynamic connectivity with the posterior cingulate cortex and supramarginal gyrus, while a court drama clip showed higher dynamic connectivity with the auditory cortex and temporoparietal junction, which suggest the involvement of specific brain processing for different movie contents. In contrast, stable connectivity was highly similar among the movie clips, and showed fewer statistical significant differences. The patterns of dynamic connectivity had higher accuracy for classifications of different movie clips than the stable connectivity and regional activity. These results support the functional significance of dynamic connectivity in reflecting functional brain changes, which could provide more functionally related information than stable connectivity.

scholarly journals ExTaxsI: an exploration tool of biodiversity molecular data

2020 ◽  
Author(s):  
Giulia Agostinetto ◽  
Anna Sandionigi ◽  
Adam Chahed ◽  
Alberto Brusati ◽  
Elena Parladori ◽  
...  
Keyword(s):  
Molecular Data ◽  
Data Availability ◽  
Future Research ◽  
Distribution Data ◽  
Biodiversity Data ◽  
Related Information ◽  
New Information ◽  
Starting Point ◽  
Key Factor ◽  
New Research

AbstractBackgroundThe increasing availability of multi omics data is leading to continually revise estimates of existing biodiversity data. In particular, the molecular data enable to characterize novel species yet unknown and to increase the information linked to those already observed with new genomic data. For this reason, the management and visualization of existing molecular data, and their related metadata, through the implementation of easy to use IT tools have become a key point for the development of future research. The more users are able to access biodiversity related information, the greater the ability of the scientific community to expand the knowledge in this area.ResultsIn our research we have focused on the development of ExTaxsI (Exploring Taxonomies Information), an IT tool able to retrieve biodiversity data stored in NCBI databases and provide a simple and explorable visualization. Through the three case studies presented here, we have shown how an efficient organization of the data already present can lead to obtaining new information that is fundamental as a starting point for new research. Our approach was also able to highlight the limits in the distribution data availability, a key factor to consider in the experimental design phase of broad spectrum studies, such as metagenomics.ConclusionsExTaxI can easily produce explorable visualization of molecular data and its metadata, with the aim to help researchers to improve experimental designs and highlight the main gaps in the coverage of available data.

scholarly journals Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Ruedeerat Keerativittayayut ◽  
Ryuta Aoki ◽  
Mitra Taghizadeh Sarabi ◽  
Koji Jimura ◽  
Kiyoshi Nakahara
Keyword(s):  
Functional Connectivity ◽  
Human Brain ◽  
Large Scale ◽  
Brain Regions ◽  
Brain Network ◽  
Time Varying ◽  
Memory Encoding ◽  
Graph Metrics ◽  
Connectivity Patterns ◽  
Network States

Although activation/deactivation of specific brain regions has been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here, we investigated time-varying functional connectivity patterns across the human brain in periods of 30–40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memory encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding.

PRESTO, a Rapid 3D Approach for Functional MRI of Human Brain

1996 ◽  
pp. 105-110 ◽  
Author(s):  
C. T. W. Moonen ◽  
P. van Gelderen ◽  
N. Ramsey ◽  
G. Liu ◽  
J. H. Duyn ◽  
...  
Keyword(s):  
Human Brain ◽  
Functional Mri

scholarly journals Human frontoparietal cortex represents behaviorally relevant target status based on abstract object features

2019 ◽  
Vol 121 (4) ◽  
pp. 1410-1427 ◽  
Author(s):  
Margaret Henderson ◽  
John T. Serences
Keyword(s):  
Frontal Cortex ◽  
Functional Mri ◽  
Irrelevant Dimension ◽  
Target Recognition ◽  
Human Subjects ◽  
Object Identity ◽  
Neural Basis ◽  
Related Information ◽  
Parking Lot ◽  
High Level

Searching for items that are useful given current goals, or “target” recognition, requires observers to flexibly attend to certain object properties at the expense of others. This could involve focusing on the identity of an object while ignoring identity-preserving transformations such as changes in viewpoint or focusing on its current viewpoint while ignoring its identity. To effectively filter out variation due to the irrelevant dimension, performing either type of task is likely to require high-level, abstract search templates. Past work has found target recognition signals in areas of ventral visual cortex and in subregions of parietal and frontal cortex. However, target status in these tasks is typically associated with the identity of an object, rather than identity-orthogonal properties such as object viewpoint. In this study, we used a task that required subjects to identify novel object stimuli as targets according to either identity or viewpoint, each of which was not predictable from low-level properties such as shape. We performed functional MRI in human subjects of both sexes and measured the strength of target-match signals in areas of visual, parietal, and frontal cortex. Our multivariate analyses suggest that the multiple-demand (MD) network, including subregions of parietal and frontal cortex, encodes information about an object’s status as a target in the relevant dimension only, across changes in the irrelevant dimension. Furthermore, there was more target-related information in MD regions on correct compared with incorrect trials, suggesting a strong link between MD target signals and behavior. NEW & NOTEWORTHY Real-world target detection tasks, such as searching for a car in a crowded parking lot, require both flexibility and abstraction. We investigated the neural basis of these abilities using a task that required invariant representations of either object identity or viewpoint. Multivariate decoding analyses of our whole brain functional MRI data reveal that invariant target representations are most pronounced in frontal and parietal regions, and the strength of these representations is associated with behavioral performance.

Analysis of functional MRI data based on an estimation of the hemodynamic response in the human brain

2004 ◽  
Vol 139 (1) ◽  
pp. 91-98 ◽  
Author(s):  
Sung-Ki Lee ◽  
Hyo Woon Yoon ◽  
Jun-Young Chung ◽  
Myung-Sung Song ◽  
HyunWook Park
Keyword(s):  
Human Brain ◽  
Functional Mri ◽  
Hemodynamic Response

A New Concept for Requirements Driven Project Planning in an Integrated PLM Environment

2008 ◽  
Author(s):  
Michael Maletz ◽  
Dan Brisson ◽  
Yong Zeng
Keyword(s):  
Process Model ◽  
Project Planning ◽  
Domain Specific ◽  
Related Information ◽  
System A ◽  
Requirements Modeling ◽  
Methodical Approach ◽  
Key Factor ◽  
Natural Language Requirements ◽  
Product And Process

Integration in today’s heterogeneous PLM environments is a key factor in all development phases. This paper describes a methodical approach to integrating requirements modeling into a PLM environment. The specific focus of integration aspects is on project planning of complex mechatronic products with recurrent character based on requirements specification documents. Function and process orientation serves as a basis for the integration. It is discussed how development projects teams can benefit by generating project plans including resource estimations and predefined interfaces to bordering disciplines along the development process. With the help of semantic parsing methods of natural language requirements and through a generic classification system a requirement based product and process model is generated. This model is then taken as the basis for deriving product and process related information. Through domain specific ontology’s generic project and resource plans are generated with the help of the proposed methodology.

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